{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Control Ops Tutorial\n",
    "\n",
    "In this tutorial we show how to use control flow operators in Caffe2 and give some details about their underlying implementations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conditional Execution Using NetBuilder\n",
    "\n",
    "Let's start with conditional operator. We will demonstrate how to use it in two Caffe2 APIs used for building nets: `NetBuilder` and `brew`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:This caffe2 python run does not have GPU support. Will run in CPU only mode.\n",
      "WARNING:root:Debug message: No module named caffe2_pybind11_state_gpu\n"
     ]
    }
   ],
   "source": [
    "from __future__ import absolute_import\n",
    "from __future__ import division\n",
    "from __future__ import print_function\n",
    "from __future__ import unicode_literals\n",
    "\n",
    "from caffe2.python import workspace\n",
    "from caffe2.python.core import Plan, to_execution_step, Net\n",
    "from caffe2.python.net_builder import ops, NetBuilder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the first example, we define several blobs and then use the 'If' operator to set the value of one of them conditionally depending on values of other blobs.\n",
    "\n",
    "The pseudocode for the conditional examples we will implement is as follows:\n",
    "\n",
    "    if (x > 0):\n",
    "        y = 1\n",
    "    else:\n",
    "        y = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "with NetBuilder() as nb:\n",
    "    # Define our constants\n",
    "    ops.Const(0.0, blob_out=\"zero\")\n",
    "    ops.Const(1.0, blob_out=\"one\")\n",
    "    ops.Const(0.5, blob_out=\"x\")\n",
    "    ops.Const(0.0, blob_out=\"y\")\n",
    "    # Define our conditional sequence\n",
    "    with ops.IfNet(ops.GT([\"x\", \"zero\"])):\n",
    "        ops.Copy(\"one\", \"y\")\n",
    "    with ops.Else():\n",
    "        ops.Copy(\"zero\", \"y\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note the usage of `NetBuilder`'s `ops.IfNet` and `ops.Else` calls: `ops.IfNet` accepts a blob reference or blob name as an input, it expects an input blob to have a scalar value convertible to bool. Note that the optional `ops.Else` is at the same level as `ops.IfNet` and immediately follows the corresponding `ops.IfNet`. Let's execute the resulting net (execution step) and check the values of the blobs.\n",
    "\n",
    "Note that since x = 0.5, which is indeed greater than 0, we should expect y = 1 after execution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x =  0.5\n",
      "y =  1.0\n"
     ]
    }
   ],
   "source": [
    "# Initialize a Plan\n",
    "plan = Plan('if_net_test')\n",
    "# Add the NetBuilder definition above to the Plan\n",
    "plan.AddStep(to_execution_step(nb))\n",
    "# Initialize workspace for blobs\n",
    "ws = workspace.C.Workspace()\n",
    "# Run the Plan\n",
    "ws.run(plan)\n",
    "# Fetch some blobs and print\n",
    "print('x = ', ws.blobs[\"x\"].fetch())\n",
    "print('y = ', ws.blobs[\"y\"].fetch())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before going further, it's important to understand the semantics of execution blocks ('then' and 'else' branches in the example above), i.e. handling of reads and writes into global (defined outside of the block) and local (defined inside the block) blobs.\n",
    "\n",
    "`NetBuilder` uses the following set of rules:\n",
    "\n",
    " - In `NetBuilder`'s syntax, a blob's declaration and definition occur at the same time - we define an operator which writes its output into a blob with a given name.\n",
    " \n",
    " - `NetBuilder` keeps track of all operators seen before the current execution point in the same block and up the stack in parent blocks.\n",
    " \n",
    " - If an operator writes into a previously unseen blob, it creates a **local** blob that is visible only within the current block and the subsequent children blocks. Local blobs created in a given block are effectively deleted when we exit the block. Any write into previously defined (in the same block or in the parent blocks) blob updates an originally created blob and does not result in the redefinition of a blob.\n",
    " \n",
    " - An operator's input blobs have to be defined earlier in the same block or in the stack of parent blocks. \n",
    " \n",
    " \n",
    "As a result, in order to see the values computed by a block after its execution, the blobs of interest have to be defined outside of the block. This rule effectively forces visible blobs to always be correctly initialized.\n",
    "\n",
    "To illustrate concepts of block semantics and provide a more sophisticated example, let's consider the following net:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "with NetBuilder() as nb:\n",
    "    # Define our constants\n",
    "    ops.Const(0.0, blob_out=\"zero\")\n",
    "    ops.Const(1.0, blob_out=\"one\")\n",
    "    ops.Const(2.0, blob_out=\"two\")\n",
    "    ops.Const(1.5, blob_out=\"x\")\n",
    "    ops.Const(0.0, blob_out=\"y\")\n",
    "    # Define our conditional sequence\n",
    "    with ops.IfNet(ops.GT([\"x\", \"zero\"])):\n",
    "        ops.Copy(\"x\", \"local_blob\")  # create local_blob using Copy -- this is not visible outside of this block\n",
    "        with ops.IfNet(ops.LE([\"local_blob\", \"one\"])):\n",
    "            ops.Copy(\"one\", \"y\")\n",
    "        with ops.Else():\n",
    "            ops.Copy(\"two\", \"y\")\n",
    "    with ops.Else():\n",
    "        ops.Copy(\"zero\", \"y\")\n",
    "        # Note that using local_blob would fail here because it is outside of the block in\n",
    "        # which it was created"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we execute this, we expect that y == 2.0, and that `local_blob` will not exist in the workspace."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x =  1.5\n",
      "y =  2.0\n"
     ]
    }
   ],
   "source": [
    "# Initialize a Plan\n",
    "plan = Plan('if_net_test_2')\n",
    "# Add the NetBuilder definition above to the Plan\n",
    "plan.AddStep(to_execution_step(nb))\n",
    "# Initialize workspace for blobs\n",
    "ws = workspace.C.Workspace()\n",
    "# Run the Plan\n",
    "ws.run(plan)\n",
    "# Fetch some blobs and print\n",
    "print('x = ', ws.blobs[\"x\"].fetch())\n",
    "print('y = ', ws.blobs[\"y\"].fetch())\n",
    "# Assert that the local_blob does not exist in the workspace\n",
    "# It should have been destroyed because of its locality\n",
    "assert \"local_blob\" not in ws.blobs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conditional Execution Using Brew Module\n",
    "\n",
    "Brew is another Caffe2 interface used to construct nets. Unlike `NetBuilder`, `brew` does not track the hierarchy of blocks and, as a result, we need to specify which blobs are considered local and which blobs are considered global when passing 'then' and 'else' models to an API call.\n",
    "\n",
    "Let's start by importing the necessary items for the `brew` API."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from caffe2.python import brew\n",
    "from caffe2.python.workspace import FeedBlob, RunNetOnce, FetchBlob\n",
    "from caffe2.python.model_helper import ModelHelper"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use the Caffe2's `ModelHelper` class to define and represent our models, as well as contain the parameter information about the models. Note that a `ModelHelper` object has two underlying nets:\n",
    "\n",
    "    (1) param_init_net: Responsible for parameter initialization\n",
    "    (2) net: Contains the main network definition, i.e. the graph of operators that the data flows through\n",
    "\n",
    "Note that `ModelHelper` is similar to `NetBuilder` in that we define the operator graph first, and actually run later. With that said, let's define some models to act as conditional elements, and use the `brew` module to form the conditional statement that we want to run. We will construct the same statement used in the first example above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize model, which will represent our main conditional model for this test\n",
    "model = ModelHelper(name=\"test_if_model\")\n",
    "\n",
    "# Add variables and constants to our conditional model; notice how we add them to the param_init_net\n",
    "model.param_init_net.ConstantFill([], [\"zero\"], shape=[1], value=0.0)\n",
    "model.param_init_net.ConstantFill([], [\"one\"], shape=[1], value=1.0)\n",
    "model.param_init_net.ConstantFill([], [\"x\"], shape=[1], value=0.5)\n",
    "model.param_init_net.ConstantFill([], [\"y\"], shape=[1], value=0.0)\n",
    "\n",
    "# Add Greater Than (GT) conditional operator to our model\n",
    "#  which checks if \"x\" > \"zero\", and outputs the result in the \"cond\" blob\n",
    "model.param_init_net.GT([\"x\", \"zero\"], \"cond\")\n",
    "\n",
    "# Initialize a then_model, and add an operator which we will set to be\n",
    "#  executed if the conditional model returns True\n",
    "then_model = ModelHelper(name=\"then_test_model\")\n",
    "then_model.net.Copy(\"one\", \"y\")\n",
    "\n",
    "# Initialize an else_model, and add an operator which we will set to be\n",
    "#  executed if the conditional model returns False\n",
    "else_model = ModelHelper(name=\"else_test_model\")\n",
    "else_model.net.Copy(\"zero\", \"y\")\n",
    "\n",
    "# Use the brew module's handy cond operator to facilitate the construction of the operator graph\n",
    "brew.cond(\n",
    "    model=model,                               # main conditional model\n",
    "    cond_blob=\"cond\",                          # blob with condition value\n",
    "    external_blobs=[\"x\", \"y\", \"zero\", \"one\"],  # data blobs used in execution of conditional\n",
    "    then_model=then_model,                     # pass then_model\n",
    "    else_model=else_model)                     # pass else_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we run the model, let's use Caffe2's graph visualization tool `net_drawer` to check if the operator graph makes sense."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 8,
     "metadata": {
      "image/png": {
       "width": 800
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from caffe2.python import net_drawer\n",
    "from IPython import display\n",
    "graph = net_drawer.GetPydotGraph(model.net, rankdir=\"LR\")\n",
    "display.Image(graph.create_png(), width=800)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's run the net! When using `ModelHelper`, we must first run the `param_init_net` to initialize paramaters, then we execute the main `net`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x =  [0.5]\n",
      "y =  [1.]\n"
     ]
    }
   ],
   "source": [
    "# Run param_init_net once\n",
    "RunNetOnce(model.param_init_net)\n",
    "# Run main net (once in this case)\n",
    "RunNetOnce(model.net)\n",
    "# Fetch and examine some blobs\n",
    "print(\"x = \", FetchBlob(\"x\"))\n",
    "print(\"y = \", FetchBlob(\"y\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loops Using NetBuilder\n",
    "\n",
    "Another important control flow operator is 'While', which allows repeated execution of a fragment of net. Let's consider `NetBuilder`'s version first.\n",
    "\n",
    "The pseudocode for this example is:\n",
    "\n",
    "    i = 0\n",
    "    y = 0\n",
    "    while (i <= 7):\n",
    "        y = i + y\n",
    "        i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "with NetBuilder() as nb:\n",
    "    # Define our variables\n",
    "    ops.Const(0, blob_out=\"i\")\n",
    "    ops.Const(0, blob_out=\"y\")\n",
    "    \n",
    "    # Define loop code and conditions\n",
    "    with ops.WhileNet():\n",
    "        with ops.Condition():\n",
    "            ops.Add([\"i\", ops.Const(1)], [\"i\"])\n",
    "            ops.LE([\"i\", ops.Const(7)])\n",
    "        ops.Add([\"i\", \"y\"], [\"y\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As with the 'If' operator, standard block semantic rules apply. Note the usage of `ops.Condition` clause that should immediately follow `ops.WhileNet` and contains code that is executed before each iteration. The last operator in the condition clause is expected to have a single boolean output that determines whether the other iteration is executed.\n",
    "\n",
    "In the example above we increment the counter (\"i\") before each iteration and accumulate its values in \"y\" blob, the loop's body is executed 7 times, the resulting blob values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "i =  8\n",
      "y =  28\n"
     ]
    }
   ],
   "source": [
    "# Initialize a Plan\n",
    "plan = Plan('while_net_test')\n",
    "# Add the NetBuilder definition above to the Plan\n",
    "plan.AddStep(to_execution_step(nb))\n",
    "# Initialize workspace for blobs\n",
    "ws = workspace.C.Workspace()\n",
    "# Run the Plan\n",
    "ws.run(plan)\n",
    "# Fetch blobs and print\n",
    "print(\"i = \", ws.blobs[\"i\"].fetch())\n",
    "print(\"y = \", ws.blobs[\"y\"].fetch())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loops Using Brew Module\n",
    "\n",
    "Now let's take a look at how to replicate the loop above using the `ModelHelper`+`brew` interface."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize model, which will represent our main conditional model for this test\n",
    "model = ModelHelper(name=\"test_while_model\")\n",
    "\n",
    "# Add variables and constants to our model\n",
    "model.param_init_net.ConstantFill([], [\"i\"], shape=[1], value=0)\n",
    "model.param_init_net.ConstantFill([], [\"one\"], shape=[1], value=1)\n",
    "model.param_init_net.ConstantFill([], [\"seven\"], shape=[1], value=7)\n",
    "model.param_init_net.ConstantFill([], [\"y\"], shape=[1], value=0)\n",
    "\n",
    "# Initialize a loop_model that represents the code to run inside of loop\n",
    "loop_model = ModelHelper(name=\"loop_test_model\")\n",
    "loop_model.net.Add([\"i\", \"y\"], [\"y\"])\n",
    "\n",
    "# Initialize cond_model that represents the conditional test that the loop\n",
    "#  abides by, as well as the incrementation step\n",
    "cond_model = ModelHelper(name=\"cond_test_model\")\n",
    "cond_model.net.Add([\"i\", \"one\"], \"i\")\n",
    "cond_model.net.LE([\"i\", \"seven\"], \"cond\")\n",
    "\n",
    "# Use brew's loop operator to facilitate the creation of the loop's operator graph\n",
    "brew.loop(\n",
    "    model=model,             # main model that contains data\n",
    "    cond_blob=\"cond\",        # explicitly specifying condition blob\n",
    "    external_blobs=[\"cond\", \"i\", \"one\", \"seven\", \"y\"], # data blobs used in execution of the loop\n",
    "    loop_model=loop_model,   # pass loop_model\n",
    "    cond_model=cond_model    # pass condition model (optional)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once again, let's visualize the net using the `net_drawer`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 13,
     "metadata": {
      "image/png": {
       "width": 800
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph = net_drawer.GetPydotGraph(model.net, rankdir=\"LR\")\n",
    "display.Image(graph.create_png(), width=800)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we'll run the `param_init_net` and `net` and print our final blob values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "i =  [8]\n",
      "y =  [28]\n"
     ]
    }
   ],
   "source": [
    "RunNetOnce(model.param_init_net)\n",
    "RunNetOnce(model.net)\n",
    "print(\"i = \", FetchBlob(\"i\"))\n",
    "print(\"y = \", FetchBlob(\"y\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Backpropagation\n",
    "\n",
    "Both 'If' and 'While' operators support backpropagation. To illustrate how backpropagation with control ops work, let's consider the following examples in which we construct the operator graph using `NetBuilder` and obtain calculate gradients using the `AddGradientOperators` function. The first example shows the following conditional statement:\n",
    "\n",
    "    x = 1-D numpy float array\n",
    "    y = 4\n",
    "    z = 0\n",
    "    if (x > 0):\n",
    "        z = y^2\n",
    "    else:\n",
    "        z = y^3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Feed blob called x, which is simply a 1-D numpy array [0.5]\n",
    "FeedBlob(\"x\", np.array(0.5, dtype='float32'))\n",
    "\n",
    "# _use_control_ops=True forces NetBuilder to output single net as a result\n",
    "# x is external for NetBuilder, so we let nb know about it through initial_scope param\n",
    "with NetBuilder(_use_control_ops=True, initial_scope=[\"x\"]) as nb:\n",
    "    ops.Const(0.0, blob_out=\"zero\")\n",
    "    ops.Const(1.0, blob_out=\"one\")\n",
    "    ops.Const(4.0, blob_out=\"y\")\n",
    "    ops.Const(0.0, blob_out=\"z\")\n",
    "    with ops.IfNet(ops.GT([\"x\", \"zero\"])):\n",
    "        ops.Pow(\"y\", \"z\", exponent=2.0)\n",
    "    with ops.Else():\n",
    "        ops.Pow(\"y\", \"z\", exponent=3.0)\n",
    "\n",
    "# we should get a single net as output\n",
    "assert len(nb.get()) == 1, \"Expected a single net produced\"\n",
    "net = nb.get()[0]\n",
    "\n",
    "# add gradient operators for 'z' blob\n",
    "grad_map = net.AddGradientOperators([\"z\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case\n",
    "\n",
    "$$x = 0.5$$\n",
    "\n",
    "$$z = y^2 = 4^2 = 16$$\n",
    "\n",
    "We will fetch the blob `y_grad`, which was generated by the `AddGradientOperators` call above. This blob contains the gradient of blob z with respect to y. According to basic calculus:\n",
    "\n",
    "$$y\\_grad = \\frac{\\partial{z}}{\\partial{y}}y^2 = 2y = 2(4) = 8$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x =  0.5\n",
      "y =  4.0\n",
      "z =  16.0\n",
      "y_grad =  8.0\n"
     ]
    }
   ],
   "source": [
    "# Run the net\n",
    "RunNetOnce(net)\n",
    "# Fetch blobs and print\n",
    "print(\"x = \", FetchBlob(\"x\"))\n",
    "print(\"y = \", FetchBlob(\"y\"))\n",
    "print(\"z = \", FetchBlob(\"z\"))\n",
    "print(\"y_grad = \", FetchBlob(\"y_grad\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's change value of blob \"x\" to -0.5 and rerun net:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x =  -0.5\n",
      "y =  4.0\n",
      "z =  64.0\n",
      "y_grad =  48.0\n"
     ]
    }
   ],
   "source": [
    "# To re-run net with different input, simply feed new blob\n",
    "FeedBlob(\"x\", np.array(-0.5, dtype='float32'))\n",
    "RunNetOnce(net)\n",
    "print(\"x = \", FetchBlob(\"x\"))\n",
    "print(\"y = \", FetchBlob(\"y\"))\n",
    "print(\"z = \", FetchBlob(\"z\"))\n",
    "print(\"y_grad = \", FetchBlob(\"y_grad\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next and final example illustrates backpropagation on the following loop:\n",
    "\n",
    "    x = 2\n",
    "    y = 3\n",
    "    z = 2\n",
    "    i = 0\n",
    "    while (i <= 2):\n",
    "        x = x^2\n",
    "        if (i < 2):\n",
    "            y = y^2\n",
    "        else:\n",
    "            z = z^3\n",
    "        i += 1\n",
    "    s = x + y + z\n",
    "    \n",
    "Note that this code essentially computes the sum of x^4 (by squaring x twice), y^2, and z^3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "with NetBuilder(_use_control_ops=True) as nb:\n",
    "    # Define variables and constants\n",
    "    ops.Copy(ops.Const(0), \"i\")\n",
    "    ops.Copy(ops.Const(1), \"one\")\n",
    "    ops.Copy(ops.Const(2), \"two\")\n",
    "    ops.Copy(ops.Const(2.0), \"x\")\n",
    "    ops.Copy(ops.Const(3.0), \"y\")\n",
    "    ops.Copy(ops.Const(2.0), \"z\")\n",
    "    \n",
    "    # Define loop statement\n",
    "    # Computes x^4, y^2, z^3\n",
    "    with ops.WhileNet():\n",
    "        with ops.Condition():\n",
    "            ops.Add([\"i\", \"one\"], \"i\")\n",
    "            ops.LE([\"i\", \"two\"])\n",
    "        ops.Pow(\"x\", \"x\", exponent=2.0)\n",
    "        with ops.IfNet(ops.LT([\"i\", \"two\"])):\n",
    "            ops.Pow(\"y\", \"y\", exponent=2.0)\n",
    "        with ops.Else():\n",
    "            ops.Pow(\"z\", \"z\", exponent=3.0)\n",
    "    \n",
    "    # Sum s = x + y + z\n",
    "    ops.Add([\"x\", \"y\"], \"x_plus_y\")\n",
    "    ops.Add([\"x_plus_y\", \"z\"], \"s\")\n",
    "\n",
    "assert len(nb.get()) == 1, \"Expected a single net produced\"\n",
    "net = nb.get()[0]\n",
    "\n",
    "# Add gradient operators to output blob 's'\n",
    "grad_map = net.AddGradientOperators([\"s\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x =  16.0\n",
      "x_grad =  32.0\n",
      "y =  9.0\n",
      "y_grad =  6.0\n",
      "z =  8.0\n",
      "z_grad =  12.0\n"
     ]
    }
   ],
   "source": [
    "workspace.RunNetOnce(net)\n",
    "print(\"x = \", FetchBlob(\"x\"))\n",
    "print(\"x_grad = \", FetchBlob(\"x_grad\")) # derivative: 4x^3\n",
    "print(\"y = \", FetchBlob(\"y\"))\n",
    "print(\"y_grad = \", FetchBlob(\"y_grad\")) # derivative: 2y\n",
    "print(\"z = \", FetchBlob(\"z\"))\n",
    "print(\"z_grad = \", FetchBlob(\"z_grad\")) # derivative: 3z^2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation Notes\n",
    "\n",
    "On the low level, Caffe2 uses the following set of operators to implement forward and backward branching and loops:\n",
    "- If - accepts *then_net* and *else_net* nets as arguments and executes one of them, depending on input condition blob value, nets are executed **in the same** workspace;\n",
    "- While - repeats execution of *loop_net* net passed as argument, net is executed in the same workspace;\n",
    "- Do - special operator that creates a separate inner workspace, sets up blob mappings between outer and inner workspaces, and runs a net in an inner workspace;\n",
    "- CreateScope/HasScope - special operators that create and keep track of workspaces used by Do operator.\n",
    "\n",
    "Higher level libraries that implement branching and looping (e.g. in `NetBuilder`, `brew`), use these operators to build control flow, e.g. for 'If':\n",
    " - do necessary sanity checks (e.g. determine which blobs are initialized and check that subnet does not read undefined blobs)\n",
    " - wrap 'then' and 'else' branches into Do\n",
    " - setup correct blob mappings by specifying which local names are mapped to outer blobs\n",
    " - prepare scope structure, used by Do operator\n",
    "\n",
    "While 'If' and 'While' Caffe2 ops can be used directly without creating local block workspaces, we encourage users to use higher level Caffe2 interfaces that provide necessary correctness guarantees.\n",
    "\n",
    "Backpropagation for 'While' in general is expensive memory-wise - we have to save local workspace for every iteration of a block, including global blobs visible to the block. It is recommended that users use `RecurrentNetwork` operator instead in production environments."
   ]
  }
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